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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14690 条记录，以下是421-430 订阅

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

ISBN: (纸本)9783031530814

The proceedings contain 31 papers. The special focus in this conference is on Recent Trends in image processing and Pattern Recognition. The topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and Their Countermeasures;Enhancing CT image Visualization and Analysis Through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient Data Integrity: Decentralised Storage and Retrieval in Modern Healthcare systems;a Residual Learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and Their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and Thermography Based image processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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Low-Light image Enhancement Based on Retinex Reflectance Compensation 36

Low-Light Image Enhancement Based on Retinex Reflectance Com...

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36th International conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhaozheng Wang, Wujun Yang, Weiran Ma, Ningtao Sun, Mingyang Wang, Xinxin Ruan, Liangyu Yi, Ru College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)1891706594

Enhancement of low-light images is a low-level visual task aimed at improving the quality of images captured under low-light conditions. In this study, a low-light image enhancement algorithm is proposed by compensating for the reflection loss of illumination components obtained through object reflection. Specifically, the algorithm first utilizes Gaussian filtering to process the value component (V) of the image, separating the illumination component from the reflection component. Then, through a illumination compensation strategy, the illumination component is processed and combined with the reflectance component to synthesize the enhanced value component (V). Finally, an adaptive global balance strategy is applied to optimize the enhanced value component (V) to ensure that the resulting image appears more natural and conforms to human visual perception habits. Experimental results demonstrate the effectiveness and superiority of our method compared to existing traditional processing algorithms and deep learning methods, showing excellent performance in enhancing dark details of images and maintaining natural colors. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： image enhancement

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Boosting Accuracy and Reducing Time in Laser Shooting Practices: Video processing Solutions for Impact Evaluation 1

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8th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Rivas-Lalaleo, David Bautista-Naranjo, Víctor Vayas-Ortega, Germania Iza-Chango, Erika Lema-Jumbo, Pamela Bran, Carlos Universidad de las Fuerzas Armadas—ESPE Sangolquí Ecuador Universidad Indoamerica Ambato Ecuador Instituto Investigación e Innovación en Electrónica Universidad Don Bosco Soyapango El Salvador

ISBN: (数字)9789819757992

ISBN: (纸本)9789819757985

This study explores the most effective method for impact measurement in laser shooting ranges, crucial for security training, accident prevention, and cost reduction. It utilizes video surveillance and image processing algorithms in Python to accurately determine laser impact locations. The research found that employing a single target per silhouette is most efficient, minimizing errors to ±1 impact and enabling heat map generation for feedback. The combination of video surveillance and image processing significantly enhances safety and reduces costs by providing real-time data. The findings advocate for the single target approach due to its precision and the comprehensive insights gained from heat maps, maintaining accuracy within a two-hit margin. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video signal processing

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Matrix Inversion Accelerated MCU Circuit for image Recognition Pertinent Computation

Matrix Inversion Accelerated MCU Circuit for Image Recogniti...

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2023 International Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Wang, Weisheng Jin, Yu Yuan, Quan Sun, Heming Beijing University of Chemical Technology Beijing China Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350326413

As the most complex computation in the fundamental matrix manipulation, the operation of matrix inversion limits the processing capability in the field of the image recognition and the pertinent application specified circuit. The intensive computation of matrix inversion is mitigated by efficient algorithms such as the Sherman-Morrison formula and accelerated floating-point multiplication and addition, while still suffering from division operations. Here, we present a high-performance floating-point divider circuit, which is capsuled as an instructor in to a general processor Cortex-M3 MCU to efficiently improve the matrix inversion speed in image recognition. The reconstructed MCU circuits are fully implemented on the Xilinx Spartan6 FPGA platform. Compared to the Xilinx floating point IP, the proposed architecture intensively reduces the division operation time by 82%. © 2023 IEEE.

关键词： image recognition

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Deep Learning Applications in Microscopy and Holography for near-field Signal processing 13

Deep Learning Applications in Microscopy and Holography for ...

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13th IEEE International conference on Communication systems and Network Technologies, CSNT 2024

作者： Mohseni, Milad Thilagham, K.T. Aravinda, K.A. Kumar, B Santhosh Nagpal, Amandeep Geetha, B.T. Islamic Azad University Department of Computer and Engineering Tabriz Branch Tabriz Iran Government College of Engineering. Salem Metallurgical Engineering Tamilnadu India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Simats Department of Ece Tamilnadu India

ISBN: (纸本)9798350305463

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms - Enhanced imageNet (EINet), HoloReconGAN, and QuantSegNet - are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced imageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result. © 2024 IEEE.

关键词： image enhancement

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image Based Security System in cloud resource Scheduling 5

Image Based Security System in cloud resource Scheduling

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5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chakraborty, Koushik Dora Pravina, C.T. Arya, Nancy Sharma, Sunil Rakesh, Shanu Kuttan Singh, Arvind Kunar Adamas University Kolkata India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Chouksey Engineering College Bilaspur Chhattisgarh India Department of Computer Science and Engineering S.V.N.I.E.T Barabanki India

ISBN: (纸本)9798400709418

The popularity of cloud computing in scheduling work processes, particularly logical work processes, is growing. The cloud computing environment may encounter significant issues with execution time and cost during resource allocation, resulting in disruptions in customer support quality. Text Passwords are right now a totally utilized insistence part, however graphical passwords and biometrics are likewise utilized. However, we really want to improve both comfort and security in any system to make it extremely secure. image-based security systems leverage visual information and image processing techniques to enhance security measures. These systems use images or video streams from cameras to monitor, detect, and respond to security threats. Most likely, the framework employs our algorithm to improve performance. It utilizes parts of both Graphical and Text Secret phrase plans. Advanced image processing algorithms analyze captured images for various security-related purposes, such as object detection, facial recognition, and anomaly detection. In contrast to the most well-known scheduling algorithms, such as multi-objective scheduling (MOS) and cost-conscious scheduling, the various scheduling algorithms in this proposed hybrid algorithm mannequin are used to make cloud services secure and highly available to end users. Identifying and verifying individuals based on facial features. This is used for access control, identity verification, and tracking individuals of interest. Analyzing images to detect unauthorized persons or objects in restricted areas, triggering alerts or alarms. In this investigation, we attempt to demonstrate the execution of various algorithm systems for the most well-known three static booking calculations. All the calculations are working out the all-out execution cost and timing of various calculations. © 2023 ACM.

关键词： Security systems

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Research on Radar Imaging Method Based on Tensor Neural Network 16

Research on Radar Imaging Method Based on Tensor Neural Netw...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Tian Liu, Nan Yang, Minglei National Key Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781510689251

LFM signals, as the most commonly used form of signals in radar systems, are of significant research value. At this stage, compressed sensing (CS) has significant potential to improve the image quality of radar imaging. However, the existing CS theories have limited ability to improve the imaging capability of LFM signals, and always suffer from low imaging efficiency, parameters need to be manually adjusted, and poor suppression of side flaps. In this paper, we propose a neural network imaging algorithm based on tensor compressed perception, called 2D-ISTA-Net. this algorithm accomplishes distance-azimuth 2D radar imaging by combining a model-based iterative threshold shrinkage (ISTA) algorithm and a data-driven deep learning model. First, the sparse recovery model of the LFM signal is obtained analytically, and then, the 2D-ISTA optimization algorithm is unfolded into a deep network structure in which all parameters are optimized based on data-driven auto-learning instead of manual tuning. Subsequently, the network learning is trained on the scattering point images in the training set to obtain a 2D-ISTA-Net network with autonomous parameter tuning capability. Furthermore, we compared this algorithm with some existing CS algorithms. The data show that 2D-ISTA-Net significantly improves the computing power and has a lower side flap level compared with the traditional CS algorithm. Experiments show that 2D-ISTA-Net has good reconstruction performance. © 2025 SPIE.

关键词： Radar imaging

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Implementation of an Efficient image Inpainting Algorithm using Optimization Techniques 6th

Implementation of an Efficient Image Inpainting Algorithm us...

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6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Revathi, K. Janardhana Rao, B. Odugu, Venkata Krishna Gade, Harish Babu Sphoorthy Engineering College Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819984503

To repair the demolished images and remove the particular unnecessary objects in the image, optimized image inpainting techniques are required. In this work, a novel exemplar-based image inpainting technique is suggested. In this technique, the patch priority is computed using the regulation factor and coefficients. Two optimization techniques as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are employed to attain the optimal values of regularization factor and coefficients. The best exemplar patch selection is carried out by calculating the sum of the absolute difference between the patches. Performance measures including peak-signal-to-noise ratio (PSNR), mean square error (MSE), and Structural Similarity Index (SSIM) are tested using the suggested image inpainting process on images based on datasets. These results are compared with the available inpainting methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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image Reconstruction Leveraging Time-Event Feature Fusion for Event Cameras 7

Image Reconstruction Leveraging Time-Event Feature Fusion fo...

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7th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Jian Wu, Sheng Liu, Wenwei Feng, Hui Hu, Bo School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute of Intelligent Electronics & Systems Shanghai200433 China

ISBN: (纸本)9798350350890

The event cameras generate asynchronous event sequences, rendering most existing image-based algorithms in-applicable for direct use and processing. Therefore, the development of a simulator that utilizes event streams captured by an event camera as input and reconstructs image sequences as output is crucial for subsequent tasks such as high-speed moving object tracking, object recognition and detection, and SLAM. Despite the extensive research and attention garnered by deep learning-based image sequence generation, there remains a problem of local representation information loss during the reconstruction process from event streams to image sequences. Existing solutions, whether based on Stacking Based on Time (SBT) or Stacking Based on Events (SBE), exhibit significant limitations when using a single stacking approach. To address the insufficiency in feature representation capability, this paper proposes a visual image conversion method based on multi-view feature fusion, effectively transforming event streams into traditional image sequences. Extensive experiments and dataset validations, utilizing multiple evaluation metrics including both global and local assessments, demonstrate the advantages of our proposed method. © 2024 IEEE.

关键词： image reconstruction

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Deep Learning-Based Noise Reduction Techniques in Electronic Signal processing 9

Deep Learning-Based Noise Reduction Techniques in Electronic...

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9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 IEEE.

关键词： Recurrent neural networks

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